data/example_protein/run.sh

#!/bin/bash
module purge
module load sghpc-mpi-gcc/26.3
#module load compiler/dtk/25.04 
source /public/software/apps/anaconda3/2023.09/etc/profile.d/conda.sh
conda activate model_bio
export WANDB_MODE=disabled
export WANDB_SILENT=true
export WANDB_DISABLED=true


export HIP_VISIBLE_DEVICES=1

#python -u ~/.conda/envs/model_bio/bin/p1_train.py \
#    --experiment-name multievolve_example \
#    --protein-name example_protein \
#    --wt-files apex.fasta \
#    --training-dataset-fname example_dataset.csv \
#    --wandb-key dummy_key \
#    --mode test

#检查模型文件
#find /public/home/scnb9biwet/jiangqq/MULTI-evolve-main/proteins/example_protein/model_cache -name "*.pth" -o -name "*.pt"

## Step 2：Propose MULTI-evolve Variants，利用训练好的模型来推荐突变
#python -u ~/.conda/envs/model_bio/bin/p2_propose.py \
#--experiment-name multievolve_example \
#--protein-name example_protein \
#--wt-files apex.fasta \
#--training-dataset example_dataset.csv \
#--mutation-pool combo_muts.csv \
#--top-muts-per-load 3 \
#--export-name multievolve_proposals

## step 3: 生成 MULTI-assembly 寡核苷酸
python -u ~/.conda/envs/model_bio/bin/p3_assembly_design.py \
--mutations-file ../../proteins/example_protein/multievolve_proposals.csv \
--wt-fasta APEX_33overhang.fasta \
--overhang 33 \
--species human \
--oligo-direction top \
--tm 80 \
--output design